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Audio AI models are increasingly being trained on datasets that contain significant bias and offensive language. This issue raises concerns about the ethical implications of using generative audio products like song generators and voice cloning tools. While text and image AI have faced scrutiny, the audio sector has largely been overlooked in discussions about bias and copyright infringement.
William Agnew from Carnegie Mellon University highlights the need for more attention to the training data used for audio AI. The prevalence of biased language in training datasets can lead to skewed outputs, which may perpetuate harmful stereotypes. Addressing these issues is crucial for the responsible development of audio AI technologies.
• Audio AI training data often contains bias and offensive language.
• Generative audio products are gaining popularity but lack scrutiny.
Generative audio refers to AI technologies that create audio content, such as music or speech.
Bias in AI occurs when training data reflects prejudiced views, leading to unfair outputs.
Voice cloning is a technology that replicates a person's voice using AI, raising ethical concerns.
Carnegie Mellon University is involved in AI research, focusing on addressing bias in audio training datasets.
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